This repository provides the codes for paper IoU-Uniform R-CNN
- This project is based on mmdetection framework.
Please follow official installation and getting_started guides.
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [--validate] [other_optional_args]
Note:
-
Config files of IoU-Uniform R-CNN:
- configs/faster_rcnn_r50_fpn_1x_IoU_reg.py
- configs/faster_rcnn_r101_fpn_1x_IoU_reg.py
- configs/cascade_rcnn_r50_fpn_IoU_reg_1x.py
- configs/cascade_rcnn_r101_fpn_IoU_reg_1x.py
- configs/pascal_voc/faster_rcnn_r50_fpn_1x_IoU_reg_voc0712.py
- configs/pascal_voc/faster_rcnn_r50_fpn_1x_reg_separate_voc0712.py
- configs/pascal_voc/cascade_rcnn_r50_fpn_1x_IoU_reg_voc0712.py
-
We train IoU-Uniform R-CNN and accompanied detectors with 2 GPUs and 2 img/GPU. According to the Linear Scaling Rule, you need to set the learning rate proportional to the batch size if you use different GPUs or images per GPU.
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
- Release IoU-Uniform R-CNN code base
- Release trained models